Spiking neural networks (SNNs) have emerged as a promising direction in both computational neuroscience and artificial intelligence, offering advantages such as strong biological plausibility and low energy consumption on neuromorphic hardware. Despite these benefits, SNNs still face challenges in achieving state-of-the-art performance on vision tasks. Recent work has shown that hybrid rate-temporal coding strategies (particularly those incorporating bit-plane representations of images into traditional rate coding schemes) can significantly improve performance when trained with surrogate backpropagation. Motivated by these findings, this study proposes a hybrid temporal-bit spike coding method that integrates bit-plane decompositions with temporal coding principles. Through extensive experiments across multiple computer vision benchmarks, we demonstrate that blending bit-plane information with temporal coding yields competitive, and in some cases improved, performance compared to established spike-coding techniques. To the best of our knowledge, this is the first work to introduce a hybrid temporal-bit coding scheme specifically designed for surrogate gradient training of SNNs.

翻译：脉冲神经网络（SNNs）已成为计算神经科学与人工智能领域一个极具前景的研究方向，其具备较强的生物合理性和在神经形态硬件上的低能耗优势。尽管存在这些优点，SNNs在视觉任务上实现最先进性能仍面临挑战。近期研究表明，混合速率-时域编码策略（尤其是将图像的位平面表示融入传统速率编码方案的方法）在使用代理反向传播训练时能显著提升性能。受此启发，本研究提出一种混合时域-位脉冲编码方法，将位平面分解与时域编码原理相结合。通过在多个计算机视觉基准数据集上的大量实验，我们证明相较于成熟的脉冲编码技术，融合位平面信息与时域编码能够取得具有竞争力、甚至在某些情况下更优的性能。据我们所知，这是首个专门为SNNs代理梯度训练设计的混合时域-位编码方案。